基于BP神经网络模型的呼出气δ13C、δ18O同位素丰度测量方法研究

扫码查看

原文链接

万方数据

中文摘要：碳13(13C)尿素呼气试验在国内外作为检测幽门螺旋杆菌的"金标准"已被广泛采用,精准测量CO2中碳(C)和氧(O)同位素特征对疾病诊断具有重大意义.可调谐半导体激光吸收光谱技术(TDLAS)具有结构简单、响应速度快、灵敏度高等众多优点,在多个领域得到广泛应用,同时完全适用于气体同位素的测量研究.该研究面向人体呼出气体中的CO2气体检测需求,基于直接吸收光谱技术,采用中心波长为4.32 μm的量子级联激光器(QCL)结合光程为14 cm/44 mL的小容积气体吸收腔体,完成了同时测量16O12C16O、18O12C16O和16O13C16O的多组分同位素气体浓度的实验系统.基于反向传播(BP)神经网络模型,降低直接吸收光谱系统中光源稳定性和测量样品气体波动带来的噪声干扰.结果表明:基于BP神经网络模型的同位素丰度测量精度与稳定性均优于吸光度峰值比法,16O13C16O与18O12C16O的浓度测量精度分别提高约1.27与1.58倍.Allan方差分析表明,当积分时间为106 s时,采用BP神经网络模型的13C与18O同位素丰度测量精度分别为0.97‰和1.47‰,相比吸光度峰值比法测量精度提高了约2.1倍与1.2倍.充分证明了基于BP神经网络模型的同位素丰度测量方法的可行性,为研制高精度同位素丰度传感器奠定基础.

外文标题：Research on Measurement Method of δ13C and δ18O Isotopes Abundance in Exhaled Gas Based on the BP Neural Network Model

外文摘要：The 13 C urea breath test is widely used as the"gold standard"for detecting Helicobacter pylori domestically and internationally.Accurate measurements of carbon and oxygen isotope characteristics in CO2 are significant for disease diagnosis.Tunable diode laser absorption spectroscopy(TDLAS)has been widely used in multiple fields due to its simple structure,fast response speed,and high sensitivity.It is also fully suitable for the measurement and research of gas isotopes.In this study,a quantum cascade laser with a central wavelength of 4.32 μm and a small volume gas absorption cellwith an optical path of 14 cm and volume of 44 mL was used to simultaneously measure the volume concentrations of 16O12C16O,18O12C16O and 16O13C16O in CO2 based on direct absorption spectroscopy.In addition,the noise interference caused by the stability of the light source in the direct absorption spectrum system and gas sample fluctuations were reduced using the Back Propagation(BP)neural network model.The results showed that the measurement accuracy and stability of isotope abundance based on the BP neural network model were better than those of the absorbance peak ratio method.The concentration measurement accuracy of 16O13C16O and 18O12C16O increased by about 1.27 and 1.58 times,respectively.Meanwhile,the Allan variance analysis also showed that when the integration time was 106 s,the accuracy of 13C and 18O isotope abundance using the BP neural network model was 0.97‰and 1.47‰,respectively,which improved about 2.1 times and 1.2 times compared to the absorbance peak ratio method.This fully proves the feasibility of the isotope abundance measurement method based on the BP neural network model,laying the foundation for developing high-precision isotope abundance sensors.

外文关键词：

Tunable diode laser absorption spectroscopy(TDLAS)Quantum cascade laser(QCL)Backpropagation(BP)neural network modelIsotopes abundance

作者：

黄文彪、夏滑、王前进、孙鹏帅、庞涛、吴边、张志荣

展开 >

作者单位：

中国科学技术大学研究生院科学岛分院,安徽合肥 230026

中国科学院合肥物质科学研究院,安徽光学精密机械研究所,光子器件与材料安徽省重点实验室,安徽合肥 230031

中国科学院合肥物质科学研究院,安徽光学精密机械研究所,中国科学院环境光学与技术重点实验室,安徽合肥 230031

国防科技大学先进激光技术安徽省实验室,安徽合肥 230037

展开 >

关键词：

可调谐半导体激光吸收光谱技术(TDLAS) 量子级联激光器(QCL) 反向传播(BP)神经网络模型同位素丰度

基金：

国家重点研发计划项目国家重点研发计划项目国家自然科学基金项目国家自然科学基金项目国家自然科学基金项目中国科学院合肥研究院"火花"基金项目

项目编号：

2021YFB32019042022YFB3207601118743644187731142005107YZJJ2022QN02

出版年：

2024

DOI：

10.3964/j.issn.1000-0593(2024)10-2761-07

光谱学与光谱分析

中国光学学会

光谱学与光谱分析

CSTPCD北大核心

影响因子：0.897

ISSN：1000-0593

年,卷(期)：2024.44(10)